Lesson 1.3 – Use Cases that Win

📌 Introduction

Not all AI agent projects are created equal.

Success doesn’t come from choosing the most complex problem — it comes from choosing the right use case: one where an AI Agent can perform valuable, repeatable tasks that benefit the business by saving time, improving accuracy, expanding capacity, or automating human effort.

This lesson will teach you how to identify and prioritize winning use cases for your AI Agent journey — especially in the early stages of adoption.

🧠 What Makes a Great AI Agent Use Case?

A winning use case typically checks several of these boxes:

In short: start where the agent can succeed fast and safely.

🚦 Start Inside: Internal Use Cases First

A common mistake is launching AI Agents directly to customers from day one. But the best practice is to start with internal-facing agents, where employees use the AI as a copilot to augment their workflows.

Why start here?

• You control the environment (feedback, risk, user behavior)

• Employees can rate and correct agent output

• It builds organizational confidence and training data

• You gather real usage insights before exposing it to customers

🧰 Example: Support Team Copilot → Customer-Facing Chat Agent

Let’s walk through a real progression path.

🧪 Phase 1: Internal Support Copilot

• Use Case: An AI Agent is embedded in raia Copilot to assist support agents

• Capabilities:

  • Pulls relevant knowledge base articles

  • Suggests responses to customer tickets

  • Summarizes long policy documents

  • Answers internal questions like “What’s our escalation policy?”

• Value: Speeds up response time, reduces manual searching, improves first-time resolution

• Users: Human agents remain the final decision-makers

Impact: The team handles more tickets per day, and junior agents are more productive.

🚀 Phase 2: Public-Facing Live Chat Agent

Once the agent shows high accuracy, high coverage, and reliable behavior, it’s promoted to handle incoming support requests directly via:

• Live chat on the website

• Email auto-responses

• Ticket triage based on customer input

• Voice AI, if extended to IVR or phone automation

The same AI Agent is simply exposed through a different interface layer.

Impact: Now customers get instant answers 24/7, while complex cases escalate to humans.

📈 The Use Case Maturity Curve

You don’t go from zero to customer-facing autonomy overnight. Instead, think in layers:

🔍 Best Practices for Use Case Selection

1. Look for Repetition, Not Complexity Start with repetitive tasks that follow predictable logic.

2. Mine Your Support Tickets, Chat Logs, Internal Docs These often contain gold: the same questions asked again and again.

3. Align with Business Value Choose cases that reduce time, cut costs, or expand hours of service.

4. Avoid “Corner Cases” Early Skip use cases with high ambiguity, emotion, or compliance risk until later.

5. Map to Agent Architecture Readiness Ask:

   • Do we have training material (vector store)?

   • Can the agent access real-time data (tools)?

   • Do we know how the output should be formatted (instructions)?

6. Start with Support, IT, HR, and Operations These domains are structured, repetitive, and high-volume — ideal for AI automation.

🛠 Common Early Use Case Categories

✅ Key Takeaways

• Start with internal-facing agents (Copilot model) to learn, improve, and derisk.

• A great use case is repetitive, low-risk, supported by clear data, and time-intensive for humans.

• Use the Support Team progression path: internal → external → autonomous.

• Avoid high-subjectivity or compliance-heavy use cases in your first phase.

• The best AI Agent use cases save time, automate work, and scale expertise without sacrificing quality.